Offline Authentication Tokens for DDIL Secure Network Access
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing technologies lack effective methods for authenticating users in secure and isolated network environments, particularly in denied, disrupted, intermittent, and limited-bandwidth (DDIL) environments, where mobile devices with restricted access and processing limitations are disconnected for unknown time ranges.
Innovation Solution
A system and method for authenticating users in DDIL environments by analyzing user ID data, behavioral data, and machine data using machine learning and rules engines, generating offline authorization tokens, and storing data in a blockchain for verification upon reconnection, with peer-to-peer authentication for additional validation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional permission server authentication is used, then authentication can be performed with centralized verification, but the system cannot operate in disconnected or isolated network environments
Solution Approach 1:
The authentication system is segmented into offline and online components. The offline authentication module operates independently using local credentials and machine learning models, while the online module handles verification and updates. This segmentation allows the system to function reliably in disconnected environments while maintaining adaptability to different network conditions.
Solution Approach 2:
Authentication credentials, machine learning models, and authorization rules are pre-loaded onto mobile devices before disconnection occurs. This preliminary action enables the offline authentication module to operate autonomously without real-time server connectivity, resolving the contradiction between centralized verification reliability and environment adaptability.
2Adaptability or versatility
If offline authentication is implemented, then the system can operate in disconnected environments, but authentication security and threat detection capability are reduced
Solution Approach 1:
The system implements continuous feedback loops where authentication decisions are made by comparing user ID data, behavioral data, and machine data against pre-loaded historical patterns. The machine learning models provide feedback-based threat assessment, and authentication results are logged for later verification when connectivity is restored, maintaining security despite offline operation.
Solution Approach 2:
The offline authentication module performs self-service authentication by using locally stored credentials and machine learning models to verify user identities and detect threats without external assistance. This self-contained approach enables offline operation while maintaining authentication security through autonomous decision-making capabilities.
3Measurement precision
If comprehensive data analysis is performed, then authentication accuracy and threat detection improve, but processing requirements and system complexity increase
Solution Approach 1:
The system performs partial data analysis by focusing on the most critical authentication factors using pre-trained machine learning models. Rather than analyzing all possible data points, the system selectively processes user ID data, behavioral data, and machine data based on pre-determined importance weights, achieving high authentication accuracy while limiting processing complexity to what mobile devices can handle.
4Reliability
If continuous authentication monitoring is implemented, then threat detection capability improves, but energy consumption and processing load increase
Solution Approach 1:
The continuous authentication monitoring is implemented as periodic action where the system continuously collects authentication data but performs intensive analysis only at scheduled intervals or when trigger conditions are met. The machine learning models evaluate user ID data, behavioral data, and machine data periodically rather than in real-time for every action, maintaining threat detection capability while significantly reducing energy consumption and processing load on mobile devices.
Data Source
AI summary
Embodiments of the current disclosure are directed to authorizing data transfers and permissions requests in secure networks. In some embodiments, requesting users may request data transfers and access to secure networks, data, resources, documents, and the like. Continuous monitoring, risk analysis, and authorization may be performed in real time in the secure networks by utilizing statistical and machine learning algorithms as well as rules engines to determine a likelihood of the requests being a threat and determine an overall risk level associated with the threat. Furthermore, the secure networks may comprise denied, disrupted, intermittent, and limited-bandwidth (DDIL) DDIL environments that are disconnected from network environments for extended periods. As such, various request authentication techniques may be implemented in the DDIL environments.